/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package com.zhaohg.spark.examples.ml;

import org.apache.spark.ml.feature.Bucketizer;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.Metadata;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;

import java.util.Arrays;
import java.util.List;

// $example on$
// $example off$

public class JavaBucketizerExample {
    public static void main(String[] args) {
        SparkSession spark = SparkSession
                .builder()
                .appName("JavaBucketizerExample")
                .getOrCreate();
        
        // $example on$
        double[] splits = {Double.NEGATIVE_INFINITY, -0.5, 0.0, 0.5, Double.POSITIVE_INFINITY};
        
        List<Row> data = Arrays.asList(
                RowFactory.create(-999.9),
                RowFactory.create(-0.5),
                RowFactory.create(-0.3),
                RowFactory.create(0.0),
                RowFactory.create(0.2),
                RowFactory.create(999.9)
        );
        StructType schema = new StructType(new StructField[]{
                new StructField("features", DataTypes.DoubleType, false, Metadata.empty())
        });
        Dataset<Row> dataFrame = spark.createDataFrame(data, schema);
        
        Bucketizer bucketizer = new Bucketizer()
                .setInputCol("features")
                .setOutputCol("bucketedFeatures")
                .setSplits(splits);
        
        // Transform original data into its bucket index.
        Dataset<Row> bucketedData = bucketizer.transform(dataFrame);
        
        System.out.println("Bucketizer output with " + (bucketizer.getSplits().length - 1) + " buckets");
        bucketedData.show();
        // $example off$
        
        spark.stop();
    }
}


